Sleep scoring based on physiological information

ABSTRACT

Assessing the sleep quality of a user in association with an electronic device with one or more physiological sensors includes detecting an attempt by the user to fall asleep, and collecting physiological information associated with the user. The disclosed method of assessing sleep quality may include determining respective values for one or more sleep quality metrics, including a first set of sleep quality metrics associated with sleep quality of a plurality of users, and a second set of sleep quality metrics associated with historical sleep quality of the user, based at least in part on the collected physiological information and at least one wakeful resting heart rate of the user, and determining a unified score for sleep quality of the user, based at least in part on the respective values of the one or more sleep quality metrics.

CROSS REFERENCE TO RELATED APPLICATIONS

This is a continuation application of U.S. patent application Ser. No.16/155,139, filed Oct. 9, 2018, which is a continuation of U.S. patentapplication Ser. No. 15/456,494, filed on Mar. 11, 2017, now U.S. Pat.No. 10,111,615, both of which are entitled “SLEEP SCORING BASED ONPHYSIOLOGICAL INFORMATION,” which are hereby incorporated herein byreference in their entirety.

BACKGROUND Field

The present disclosure generally relates to the field of computingdevices with one or more sensors to collect physiological information ofa user.

Description of Related Art

Computing devices, such as wearable computing devices, can incorporateor interact with one or more sensors for receiving physiologicalinformation of a user, used for making health-based assessments.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments are depicted in the accompanying drawings forillustrative purposes, and should in no way be interpreted as limitingthe scope of the inventions. In addition, various features of differentdisclosed embodiments can be combined to form additional embodiments,which are part of this disclosure. Throughout the drawings, referencenumbers may be reused to indicate correspondence between referenceelements.

FIG. 1 is a block diagram illustrating an embodiment of a computingdevice in accordance with one or more embodiments.

FIG. 2A shows perspective front and side views of a wearable computingdevice in accordance with one or more embodiments.

FIG. 2B shows perspective back and side views of a wearable computingdevice in accordance with one or more embodiments.

FIG. 3 is a table of sleep quality assessment metrics and associatedphysiological data in accordance with one or more embodiments.

FIG. 4A is a block diagram of a basis of determining a unified sleepscore in accordance with one or more embodiments.

FIG. 4B is a table of a basis of determining a unified sleep score inaccordance with one or more embodiments.

FIG. 5 illustrates a networked relationship between a wearable computingdevice and an external computing device in accordance with one or moreembodiments.

FIG. 6 illustrates a system of client devices and a server system forperforming sleep quality assessment in accordance with one or moreembodiments.

FIG. 7A illustrates embodiments of wearable computing devices havingdisplays for presenting a representation of a unified sleep score inaccordance with one or more embodiments.

FIG. 7B illustrates embodiments of wearable computing devices havingtouchscreen displays for presenting a representation of a unified sleepscore in accordance with one or more embodiments.

FIG. 8 illustrates a flow diagram for a process of determining a sleepquality assessment in accordance with one or more embodiments.

FIG. 9 illustrates a flow diagram for a process of determining a sleepquality assessment in accordance with one or more embodiments.

DETAILED DESCRIPTION

The headings provided herein are for convenience only and do notnecessarily affect the scope or meaning of the claimed invention. Likereference numbers and designations in the various drawings may or maynot indicate like elements.

Although certain preferred embodiments and examples are disclosed below,inventive subject matter extends beyond the specifically disclosedembodiments to other alternative embodiments and/or uses and tomodifications and equivalents thereof. Thus, the scope of the claimsthat may arise herefrom is not limited by any of the particularembodiments described below. For example, in any method or processdisclosed herein, the acts or operations of the method or process may beperformed in any suitable sequence and are not necessarily limited toany particular disclosed sequence. Various operations may be describedas multiple discrete operations in turn, in a manner that may be helpfulin understanding certain embodiments; however, the order of descriptionshould not be construed to imply that these operations are orderdependent. Additionally, the structures, systems, and/or devicesdescribed herein may be embodied as integrated components or as separatecomponents. For purposes of comparing various embodiments, certainaspects and advantages of these embodiments are described. Notnecessarily all such aspects or advantages are achieved by anyparticular embodiment. Thus, for example, various embodiments may becarried out in a manner that achieves or optimizes one advantage orgroup of advantages as taught herein without necessarily achieving otheraspects or advantages as may also be taught or suggested herein.

Overview

As computing devices become more ubiquitous and portable, manyadvantages are being seen in the field of health monitoring anddiagnostics. Computing devices, particularly ones that can be worn orcarried by a user, may include one or more sensors to detectphysiological information about the user and/or the environment aroundthe user. This information can be used to observe, detect or diagnosevarious health conditions outside of a traditional clinic or laboratorysetting. For example, in the context of sleep therapy, a portable orwearable electronic device may be able to detect when a user moves atnight, or monitor various physiological factors such as a heart rate.Additionally, a computing device may be able to record and interpret thedetected information about the user and/or environment, to determine ahealth assessment. As in the previous example, a wearable electronicdevice may be able to record a relatively high frequency of movementwhile the user is sleeping, and generate an assessment that the user didnot have a restful period of sleep.

Assessment of sleep quality may be achieved in various ways. Forexample, a patient may be monitored overnight in a sleep laboratoryusing equipment such as electrodes and an electroencephalogram (EEG)machine. In contrast, another diagnostic technique may involve a patientperforming self-assessment of nightly quality of sleep, in a journal forexample. However, factors such as discomfort and unreliability posechallenges to these established techniques for sleep quality assessment.As a result, a reliable, user-friendly approach to determining sleepquality is needed. For example, by determining sleep quality of a userwearing a portable electronic device from the comfort of home, reliablephysiological information can be obtained and analyzed, without the useof extensive laboratory equipment.

Certain embodiments disclosed herein provide systems, devices andmethods for assessing sleep quality of a user, based on collectedphysiological and/or environmental data. Some embodiments implement acomputing device with one or more sensors for collecting physiologicaldata from the user and/or environmental data from the surroundingenvironment. For example, a portable computing device worn on the wristof a user may include a sensor to detect a heart rate of the user, aswell as a sensor to detect an external temperature. Additionally, someembodiments implement a computing device or computing system configuredto use the collected physiological and/or environmental information todetermine values of one or more sleep quality metrics, and to use thedetermined values of sleep quality metrics to create a unified sleepquality score. For example, the same portable computing device worn onthe wrist of the user may determine a value of a sleep restlessnessmetric, and determine a unified score for sleep quality of the user,using the sleep restlessness metric value.

As described herein, in some implementations, the present disclosure isrelated to biometric monitoring devices. The term “biometric monitoringdevice” is used in the present disclosure according to its broad andordinary meaning, and may be used in various contexts herein to refer toany type of biometric tracking devices, personal health monitoringdevices, portable monitoring devices, portable biometric monitoringdevices, or the like. In some embodiments, biometric monitoring devicesin accordance with the present disclosure may be wearable devices, suchas may be designed to be worn (e.g., continuously) by a person (i.e.,“user,” “wearer,” etc.). When worn, such biometric monitoring devicesmay be configured to gather data regarding activities performed by thewearer, or regarding the wearer's physiological state. Such data mayinclude data representative of the ambient environment around the weareror the wearer's interaction with the environment. For example, the datamay comprise motion data regarding the wearer's movements, ambientlight, ambient noise, air quality, etc., and/or physiological dataobtained by measuring various physiological characteristics of thewearer, such as heart rate, perspiration levels, and the like.

In some cases, a biometric monitoring device may leverage other devicesexternal to the biometric monitoring device, such as an external heartrate monitor in the form of an EKG sensor for obtaining heart rate data,or a GPS receiver in a smartphone used to obtain position data. In suchcases, the biometric monitoring device may communicate with theseexternal devices using wired or wireless communications connections. Theconcepts disclosed and discussed herein may be applied to bothstand-alone biometric monitoring devices as well as biometric monitoringdevices that leverage sensors or functionality provided in externaldevices, e.g., external sensors, sensors or functionality provided bysmartphones, etc.

In some implementations, a method of assessing sleep quality of a useris performed at one or more electronic devices (e.g., a wearablecomputing device and/or a biometric monitoring device), where at leastone electronic device has one or more processors, one or morephysiological sensors, and memory for storing programs to be executed bythe one or more processors. An electronic device may detect an initialattempt by a user to fall asleep, for example by detecting a lack ofmovement for a threshold period of time and/or other physiological orenvironmental factors such as ambient light conditions or bodytemperature. In some embodiments, physiological information of the useris collected or received, including at least one sleeping heart rate,such as an average heart rate since the attempt to fall asleep or adetected onset of sleep. Values for one or more sleep quality metricsmay be determined, based at least in part on the collected physiologicalinformation and at least one wakeful resting heart rate of the user. Forexample, a wearable computing device may collect heart rate informationof a user over the course of time between periods of sleep (e.g., whileawake), and determine an average heart rate value for periods ofrelative inactivity or minimal exertion during the day (e.g., wakefulresting heart rate). Additionally, a unified score for sleep quality ofthe user may be determined, based at least in part on the determinedsleep quality metric values.

In some implementations, the method includes presenting a representationof the unified score to the user, and/or generating this representationof the unified score. For example, a representation of the score is anumber between 1 and 100, or a qualitative assessment of good, neutralor poor sleep quality.

In some implementations, the one or more sleep quality metrics includesa first set of sleep quality metrics associated with sleep quality of aplurality of users, and a second set of sleep quality metrics associatedwith historical sleep quality of the user. In some implementations,determining the unified score for sleep quality includes determining arespective metric-score for each of the one or more sleep qualitymetrics and applying a respective weighting for each of the one or moresleep quality metrics.

In some implementations, the method includes determining a wakefulresting heart rate of the user before detecting an attempt by the userto fall asleep. For example, an electronic device worn by a userperiodically detects and records a heart rate (e.g., speed of a heartbeat) of the user during one or more periods of time while the user isawake, and determines an average, median or another representative valuefor a wakeful resting heart rate.

In some implementations, collecting physiological information about theuser includes collecting one or more sets of values associated withmovement of the user, total sleep duration, total deep sleep duration,duration of wake time after sleep onset (WASO), total rapid-eye-movement(REM) sleep duration, total light sleep duration, breathing patterns ofthe user, breathing disturbances of the user and/or temperature of theuser.

In some implementations, the method further includes collecting sleepquality feedback information from the user and/or receiving collectedsleep quality feedback, and determining the unified score for sleepquality of the user based at least in part on the sleep quality feedbackinformation. For example, a user may be prompted to answer one or morequestions about perceived sleep quality, after waking up from a periodof sleep. In this same example, the answers to the one or more questionsmay each be assigned a value, which is incorporated into thedetermination of the unified sleep score.

In some implementations, determining respective values for the one ormore sleep quality metrics includes comparing the at least one sleepingheart rate of the user and the at least one resting heart rate of theuser. For example, a particular user may have had an average wakefulresting heart rate of 75 beats per minute on a particular day, and anaverage sleeping heart rate of 45 beats per minute during thecorresponding night. In this same example, if either value is relativelydifferent from a historical average for the user, this may indicate achange in health or sleep quality.

In some implementations, the method includes detecting an onset of sleepby the user, and determining a duration of time from the attempt to fallasleep to the onset of sleep. For example, a wearable computing devicemay detect that the user has lain down in a darkened environment toattempt sleep at 11:05 pm, and has transitioned from wakefulness toeither non-rapid eye movement (NREM) sleep or rapid-eye movement (REM)sleep at 11:15 pm.

In some implementations, the method includes detecting that the user isawake after the detected onset of sleep, and determining the respectivevalues for one or more sleep quality metrics, in response to detectingthat the user is awake. For example, detection of a user entering astate of wakefulness triggers determination of one or more sleep qualitymetrics, and may also trigger generation of the unified sleep qualityscore.

In some implementations, detecting the attempt by the user to fallasleep includes detecting contact between the device and the user. Incertain embodiments, the electronic device is configured to be worn bythe user. Although certain embodiments are disclosed herein in thecontext of a wearable electronic device worn by a user, it should beunderstood that detection of physiological activity and/or determinationof sleep quality metrics and a unified sleep score in accordance withthe present disclosure may be performed by any suitably configuredelectronic device, including but not limited to a computer, a serversystem, a smart phone, a tablet, a laptop computer, and an electronicdevice configured to be placed under a user during a period of sleep(e.g., a bed, pillow, blanket or mat).

In some implementations, a method of assessing sleep quality of a useris performed at one or more electronic devices, where at least oneelectronic device has one or more processors, one or more physiologicalsensors, and memory for storing programs to be executed by the one ormore processors. An electronic device may detect an initial attempt by auser to fall asleep, and/or receive one or more signals indicating anattempt by a user to fall asleep, collect physiological informationassociated with the user and/or receive collected physiologicalinformation associated with the user, determine respective values forone or more sleep quality metrics, based at least in part on thecollected physiological information, wherein the one or more sleepquality metrics includes a first set of sleep quality metrics associatedwith sleep quality of a plurality of users, and a second set of sleepquality metrics associated with historical sleep quality of the user,and determined a unified sleep quality score based at least in part onthe values of the one or more sleep quality metrics.

In some implementations, the method includes determining the unifiedscore for sleep quality includes using a first weighting for the firstset of sleep quality metrics and a second weighting for the second setof sleep quality metrics. In some implementations, the first set ofsleep quality metrics is associated with clinical sleep quality data ofa demographic comparable to the user, and the second set of sleepquality metrics is associated with historical physiological informationabout the user for a minimum number of M days and a maximum number of Ndays.

In some implementations, collecting physiological information about theuser includes collecting at least one sleeping heart rate anddetermining respective values for the one or more sleep quality metricsincludes using at least one wakeful resting heart rate of the user.

The present disclosure includes certain embodiments of an electronicdevice comprising one or more physiological sensors, one or moreprocessors, memory and control circuitry configured to detect an attemptby the user to fall asleep, collect physiological information about theuser, including at least one sleeping heart rate, determine respectivevalues for one or more sleep quality metrics, using the collectedphysiological information and at least one wakeful resting heart rate ofthe user and determine a unified score for sleep quality of the user,using the respective values of the one or more sleep quality metrics.Additionally, the control circuitry may be configured to perform any ofthe methods described herein.

The present disclosure includes certain embodiments of a non-transitorycomputer readable storage medium storing one or more programs, the oneor more programs comprising instructions, which when executed by anelectronic device with one or more physiological sensors, cause thedevice to detect an attempt by the user to fall asleep, collectphysiological information about the user, including at least onesleeping heart rate, determine respective values for one or more sleepquality metrics, using the collected physiological information and atleast one wakeful resting heart rate of the user and determine a unifiedscore for sleep quality of the user, using the respective values of theone or more sleep quality metrics. Additionally, the non-transitorycomputer readable storage medium may include instructions to perform anyof the methods described herein.

The present disclosure includes certain embodiments of a method ofperforming sleep quality assessment at a sleep quality assessment servercomprising one or more processors, and memory for storing programs to beexecuted by the one or more processors for receiving physiologicalinformation about a user, including at least one sleeping heart rate andat least one wakeful resting heart rate of the user, determiningrespective values for one or more sleep quality metrics, using thecollected physiological information and determining a unified score forsleep quality of the user, using the respective values of the one ormore sleep quality metrics. The method may further include transmittingthe unified sleep score and/or the determined values of the sleepquality metrics to a wearable computing device and/or an externalcomputing device, such as a smart phone. Additionally, the sleep qualityassessment server may be configured to perform any of the methodsdescribed herein, with respect to an electronic device and/or a server.

In some embodiments, the method includes transmitting the determinedvalues for one or more sleep quality metrics to an electronic device. Insome embodiments, the electronic device is a wearable computing deviceand/or the electronic device is an external computing device. In someembodiments, the electronic device has a display configured to presentthe unified score and/or the determined values for one or more sleepquality metrics. In some implementations, determining the unified scorefor sleep quality includes determining a respective metric-score foreach of the one or more sleep quality metrics and using a respectiveweighting for each of the one or more sleep quality metrics. In someimplementations, the method includes receiving sleep quality feedbackinformation collected from the user (e.g., at a wearable computingdevice and/or at an external computing device), and determining theunified score for sleep quality of the user by additionally using thecollected sleep quality feedback information. In some implementations,determining respective values for the one or more sleep quality metricsincludes comparing the at least one sleeping heart rate of the user andthe at least one wakeful resting heart rate of the user. In someimplementations, the method includes receiving a trigger to determinethe unified sleep quality score (e.g., receiving notice that the user isawake or has requested the score), and determining the respective valuesfor one or more sleep quality metrics, in response to receiving thetrigger to determine the unified sleep quality score. In someimplementations, the method includes updating a profiles database withthe received physiological information and/or the determined values ofsleep quality metrics and/or the unified sleep score. In someimplementations, determining respective values for one or more sleepquality metrics (e.g., of the user) includes retrieving values for oneor more sleep quality metrics corresponding to a plurality of users(e.g., other users of a similar demographic to the user).

The present disclosure includes certain embodiments of a method ofperforming sleep quality assessment at a sleep quality assessment servercomprising one or more processors, and memory for storing programs to beexecuted by the one or more processors for receiving physiologicalinformation about a user, determining respective values for one or moresleep quality metrics using the collected physiological information,wherein the one or more sleep quality metrics includes a first set ofsleep quality metrics associated with sleep quality of a plurality ofusers, and a second set of sleep quality metrics associated withhistorical sleep quality of the user and determining a unified score forsleep quality of the user, using the respective values of the one ormore sleep quality metrics. The method may further include transmittingthe unified sleep score and/or the determined values of the sleepquality metrics to a wearable computing device and/or an externalcomputing device, such as a smart phone. Additionally, the sleep qualityassessment server may be configured to perform any of the methodsdescribed herein, with respect to an electronic device and/or a server.

Portable Computing Devices

Systems, devices and/or methods/processes in accordance with the presentdisclosure may comprise, or be implemented in connection with, abiometric monitoring device. Embodiments of the present disclosure mayprovide biometric monitoring devices configured to collect physiologicaldata of a user from one or more physiological metric sensors and/orenvironmental data from one or more environmental sensors. Embodimentsof the present disclosure may further provide biometric monitoringdevices configured to analyze and interpret the collected data and/orcommunicate with another computing device to analyze and interpret thecollected data. It is to be understood that while the concepts anddiscussion included herein are presented in the context of biometricmonitoring devices, these concepts may also be applied in other contextsas well if the appropriate hardware is available. For example, some orall of the relevant sensor functionality may be incorporated in one ormore external computing devices (e.g., smartphone) or computing systems(e.g., a server) communicatively coupled to the biometric monitoringdevice.

FIG. 1 is a block diagram illustrating an embodiment of a computingdevice 100 in accordance with one or more embodiments disclosed herein.In certain embodiments, the computing device 100 may be worn by a user10, such as with respect to embodiments in which the computing device100 is a wearable biometric or physiological monitoring device. Forexample, the computing device 100 may comprise a wearable biometricmonitoring device configured to gather data regarding activitiesperformed by the wearer, or regarding the wearer's physiological state.Such data may include data representative of the ambient environmentaround the wearer or the wearer's interaction with the environment. Forexample, the data may comprise motion data regarding the wearer'smovements, ambient light, ambient noise, air quality, etc., and/orphysiological data obtained by measuring various physiologicalcharacteristics of the wearer, such as heart rate, body temperature,blood oxygen levels, perspiration levels, and the like. Although certainembodiments are disclosed herein in the context of wearable biometricmonitoring devices, it should be understood that biometric/physiologicalmonitoring and health assessment principles and features disclosedherein may be applicable with respect to any suitable or desirable typeof computing device or combination of computing devices, whetherwearable or not.

The computing device 100 may include one or more audio and/or visualfeedback modules 130, such as electronic touchscreen display units,light-emitting diode (LED) display units, audio speakers, LED lights orbuzzers. In certain embodiments, the one or more audio and/or visualfeedback modules 130 may be associated with the front side of thecomputing device 100. For example, in wearable embodiments of thecomputing device 100, an electronic display may be configured to beexternally presented to a user viewing the computing device 100.

The computing device 100 includes control circuitry 110. Althoughcertain modules and/or components are illustrated as part of the controlcircuitry 110 in the diagram of FIG. 1, it should be understood thatcontrol circuitry associated with the computing device 100 and/or othercomponents or devices in accordance with the present disclosure mayinclude additional components and/or circuitry, such as one or more ofthe additional illustrated components of FIG. 1. Furthermore, in certainembodiments, one or more of the illustrated components of the controlcircuitry 110 may be omitted and/or different than that shown in FIG. 1and described in association therewith. The term “control circuitry” isused herein according to its broad and/ordinary meaning, and may includeany combination of software and/or hardware elements, devices orfeatures, which may be implemented in connection with operation of thecomputing device 100. Furthermore, the term “control circuitry” may beused substantially interchangeably in certain contexts herein with oneor more of the terms “controller,” “integrated circuit,” “IC,”“application-specific integrated circuit,” “ASIC,” “controller chip,” orthe like.

The control circuitry 110 may comprise one or more processors, datastorage devices, and/or electrical connections. For example, the controlcircuitry 110 may comprise one or more processors configured to executeoperational code for the computing device 100, such as firmware or thelike, wherein such code may be stored in one or more data storagedevices of the computing device 100. In one embodiment, the controlcircuitry 110 is implemented on an SoC (system on a chip), though thoseskilled in the art will recognize that other hardware/firmwareimplementations are possible.

The control circuitry 110 may comprise a sleep quality assessment module113. The sleep quality assessment module 113 may comprise one or morehardware and/or software components or features configured to make anassessment of sleep quality of a user, optionally using inputs from oneor more environmental sensors 155 (e.g., ambient light sensor) andinformation from the physiological metric module 141. In certainembodiments, the sleep assessment module 111 includes a sleep scoredetermination module 113 configured to determine a unified sleep qualityscore, using information accumulated by the sleep assessment module 111,such as the values of one or more physiological metrics determined bythe physiological metric calculation module 142 of the physiologicalmetric module 141. In certain embodiments, the physiological metricmodule 141 is optionally in communication with one or more internalphysiological sensors 140 embedded or integrated in the biometricmonitoring device 100. In certain embodiments, the physiological metricmodule 141 is optionally in communication with one or more externalphysiological sensors 145 not embedded or integrated in the biometricmonitoring device 100 (e.g., an electrode, or sensor integrated inanother electronic device). Examples of internal physiological sensors140 and external physiological sensors 145 include, but are not limitedto, sensors for measuring body temperature, heart rate, blood oxygenlevel and movement.

The computing device may further comprise one or more data storagemodules 151, which may include any suitable or desirable type of datastorage, such as solid-state memory, which may be volatile ornon-volatile. Solid-state memory of the computing device 100 maycomprise any of a wide variety of technologies, such as flash integratedcircuits, Phase Change Memory (PC-RAM or PRAM), ProgrammableMetallization Cell RAM (PMC-RAM or PMCm), Ovonic Unified Memory (OUM),Resistance RAM (RRAM), NAND memory, NOR memory, EEPROM, FerroelectricMemory (FeRAM), MRAM, or other discrete NVM (non-volatile solid-statememory) chips. The data storage 151 may be used to store system data,such as operating system data and/or system configurations orparameters. The computing device 100 may further comprise data storageutilized as a buffer and/or cash memory for operational use by thecontrol circuitry 110.

Data storage modules 151 may include various sub-modules, including, butnot limited to one or more of a sleep detection module for detecting anattempt or onset of sleep by the user 10, an information collectionmodule for managing the collection of physiological and/or environmentaldata relevant to a sleep quality assessment, a sleep quality metriccalculation module for determining values of one or more sleep qualitymetrics as described in the present disclosure, a unified scoredetermination module for determining a representation of a unified sleepquality score as described in the present disclosure, a presentationmodule for managing presentation of sleep quality assessment informationto user 10, a heart rate determination module for determining values andpatterns of one or more types of heart rates of user 10, a feedbackmanagement module for collecting and interpreting sleep quality feedbackfrom user 10.

The computing device 100 further comprises power storage 153, which maycomprise a rechargeable battery, one or more capacitors, or othercharge-holding device(s). The power stored by the power storage module153 maybe utilized by the control circuitry 110 for operation of thecomputing device 100, such as for powering the touchscreen display 130.The power storage module 153 may receive power over the host interface176 or through other means.

The computing device 100 may comprise one or more environmental sensors155. Examples of such environmental sensors 155 include, but are notlimited to sensors for measuring ambient light, external (non-body)temperature, altitude and global-positioning system (GPS) data.

The computing device 100 may further comprise one or more connectivitycomponents 170, which may include, for example, a wireless transceiver172. The wireless transceiver 172 may be communicatively coupled to oneor more antenna devices 195, which may be configured to wirelesslytransmit/receive data and/or power signals to/from the computing deviceusing, but not limited to peer-to-peer, WLAN or cellular communications.For example, the wireless transceiver 172 maybe utilized to communicatedata and/or power between the computing device 100 and an external hostsystem (not shown), which may be configured to interface with thecomputing device 100. In certain embodiments, the computing device 100may comprise additional host interface circuitry and/or components 176,such as wired interface components for communicatively coupling with ahost device or system to receive data and/or power therefrom and/ortransmit data thereto.

The connectivity circuitry 170 may further comprise user interfacecomponents 174 for receiving user input. For example, the user interface174 may be associated with one or more audio/visual feedback modules130, wherein the touchscreen display is configured to receive user inputfrom user contact therewith. The user interface module 174 may furthercomprise one or more buttons or other input components or features.

The connectivity circuitry 170 may further comprise the host interface176, which may be, for example, an interface for communicating with ahost device or system (not shown) over a wired or wireless connection.The host interface 176 may be associated with any suitable or desirablecommunication protocol and/or physical connector, such as UniversalSerial Bus (USB), Micro-USB, WiFi, Bluetooth, FireWire, PCIe, or thelike. For wireless connections, the host interface 176 may beincorporated with the wireless transceiver 172.

Although certain functional modules and components are illustrated anddescribed herein, it should be understood that authentication managementfunctionality in accordance with the present disclosure may beimplemented using a number of different approaches. For example, in someimplementations the control circuitry 110 may comprise one or moreprocessors controlled by computer-executable instructions stored inmemory so as to provide functionality such as is described herein. Inother implementations, such functionality may be provided in the form ofone or more specially-designed electrical circuits. In someimplementations, such functionality may be provided by one or moreprocessors controlled by computer-executable instructions stored in amemory coupled with one or more specially-designed electrical circuits.Various examples of hardware that may be used to implement the conceptsoutlined herein include, but are not limited to, application specificintegrated circuits (ASICs), field-programmable gate arrays (FPGAs), andgeneral-purpose microprocessors coupled with memory that storesexecutable instructions for controlling the general-purposemicroprocessors.

Wearable Computing Devices

In some implementations, a portable computing device, such as computingdevice 100 of FIG. 1, may be designed so that it can be inserted into awearable case or into one or more of multiple different wearable cases(e.g., a wristband case, a belt-clip case, a pendant case, a caseconfigured to be attached to a piece of exercise equipment such as abicycle, etc.). In other implementations, a portable computing devicemay be designed to be worn in limited manners, such as a computingdevice that is integrated into a wristband in a non-removable manner,and may be intended to be worn specifically on a person's wrist (orperhaps ankle). Irrespective of configuration, wearable computingdevices having one or more physiological and/or environmental sensorsmay be configured to collect physiological and/or environmental data inaccordance with various embodiments disclosed herein. Wearable computingdevices may also be configured to analyze and interpret collectedphysiological and/or environmental data to perform a sleep qualityassessment of a user (e.g., wearer) of the computing device, or may beconfigured to communicate with another computing device or server thatperforms the sleep quality assessment.

Wearable computing devices according to embodiments and implementationsdescribed herein may have shapes and sizes adapted for coupling to(e.g., secured to, worn, borne by, etc.) the body or clothing of a user.An example of a wearable computing device 201 is shown in FIG. 2A. FIG.2A shows perspective front and side views of the wearable computingdevice 201. The wearable computing device 201 includes both a computingdevice 200, as well as a band portion 207. In certain embodiments, theband portion 207 includes first and second portions that may beconnected by a clasp portion 209. The computing device portion 200 maybe insertable, and may have any suitable or desirable dimensions.Wearable computing devices may generally be relatively small in size soas to be unobtrusive for the wearer, and therefore, an optionaltouchscreen display 230 may be relatively small in size relative tocertain other computing devices. The computing device 200 may bedesigned to be able to be worn without discomfort for long periods oftime and to not interfere with normal daily activity or with sleep.

The electronic display 230 may comprise any type of electronic displayknown in the art. For example, the display 230 may be a liquid crystaldisplay (LCD) or organic light emitting diode (OLED) display, such as atransmissive LCD or OLED display. The electronic display 230 may beconfigured to provide brightness, contrast, and/or color saturationfeatures according to display settings maintained by control circuitryand/or other internal components/circuitry of the computing device 200.

The touchscreen 230 may be a capacitive touchscreen, such as a surfacecapacitive touchscreen or a projective capacitive touch screen, whichmay be configured to respond to contact with electrical charge-holdingmembers or tools, such as a human finger.

Wearable computing devices, such as biometric monitoring devices, inaccordance with the present disclosure may incorporate one or moreexisting functional components or modules designed for determining oneor more physiological metrics associated with a user (e.g., wearer) ofthe device, such as a heart rate sensor (e.g., photoplethysmographsensor), body temperature sensor, environment temperature sensor or thelike. Such components/modules may be disposed or associated with anunderside/backside of the biometric monitoring device, and may be incontact or substantially in contact with human skin when the biometricmonitoring device is worn by a user. For example, where the biometricmonitoring device is worn on the user's wrist, the physiological metriccomponent(s)/module(s) may be associated with an underside/backside ofthe device substantially opposite the display and touching the arm ofthe user.

In one embodiment, the physiological and/or environmental sensors(sources and/or detectors) may be disposed on an interior or skin-sideof the wearable computing device (i.e., a side of the computing devicethat contacts, touches, and/or faces the skin of the user (hereinafter“skin-side”). In another embodiment, the physiological and/orenvironmental sensors may be disposed on one or more sides of thedevice, including the skin-side and one or more sides of the device thatface or are exposed to the ambient environment (environmental side).

FIG. 2B is a perspective back and side view of the wearable biometricmonitoring device 201 of FIG. 2A. The wearable biometric monitoringdevice 201 comprises a band portion 207, which may be configured to belatched or secured about a user's arm or other appendage via asecurement mechanism of any suitable or desirable type. For example, theband 207 may be secured using a hook and loop clasp component 209. Incertain embodiments, the band 207 is designed with shape memory topromote wrapping around the user's arm.

The wearable biometric monitoring device 201 includes a biometricmonitoring device component 200, which may be at least partially securedto the band 207. The view of FIG. 2B shows a backside 206 (also referredto herein as the “underside”) of the biometric monitoring device 200,which may generally face and/or contact skin or clothing associated withthe user's arm, for example. The terms “backside” and “underside” areused herein according to their broad and ordinary meaning, and may beused in certain contexts to refer to a side, panel, region, component,portion and/or surface of a biometric monitoring device that ispositioned and/or disposed substantially opposite to a user displayscreen, whether exposed externally of the device, or at least partiallyinternal to an electronics package or housing of the device.

The wearable biometric monitoring device 201 may include one or morebuttons 203, which may provide a mechanism for user input. The wearablebiometric monitoring device 201 may further comprise a device housing,which may comprise one or more of steel, aluminum, plastic, and/or otherrigid structure. The housing 208 may serve to protect the biometricmonitoring device 200 and/or internal electronics/components associatedtherewith from physical damage and/or debris. In certain embodiments,the housing 208 is at least partially waterproof.

The backside 206 of the biometric monitoring device 200 may have anoptical physiological metric sensor 243 associated therewith, which maycomprise one or more sensor components, such as one or more lightsources 240 and/or light detectors 245, the collection of which mayrepresent an example of an internal physiological metric sensor 140 ofFIG. 1. In certain embodiments, the optical physiological metric sensor243 comprises a protrusion form protruding from the back surface of thebiometric monitoring device 200. The sensor components may be used todetermine one or more physiological metrics of a user wearing thewearable biometric monitoring device 201. For example, the opticalphysiological sensor components associated with the sensor 243 may beconfigured to provide readings used to determine heart rate (e.g., inbeats-per-minute (BPM)), blood oxygenation (e.g., SpO₂), blood pressure,or other metric. In certain embodiments, the biometric monitoring device200 further includes an electrical charger mating recess 207.

Although the sensor 243 is illustrated as comprising a protrusion fromcertain figures herein, it should be understood that backside sensormodules in accordance with the present disclosure may or may not beassociated with a protrusion form. In certain embodiments, theprotrusion form on the backside of the device may be designed to engagethe skin of the user with more force than the surrounding device body.In certain embodiments, an optical window or light-transmissivestructure may be incorporated in a portion of the protrusion 243. Thelight emitter(s) 240 and/or detector(s) 245 of the sensor module 243 maybe disposed or arranged in the protrusion 243 near the window orlight-transmissive structure. As such, when attached to the user's body,the window portion of the protrusion 243 of the biometric monitoringdevice 200 may engage the user's skin with more force than thesurrounding device body, thereby providing a more secure physicalcoupling between the user's skin and the optical window. That is, theprotrusion 243 may cause sustained contact between the biometricmonitoring device and the user's skin that may reduce the amount ofstray light measured by the photodetector 245, decrease relative motionbetween the biometric monitoring device 200 and the user, and/or provideimproved local pressure to the user's skin, some or all of which mayincrease the quality of the cardiac signal of interest generated by thesensor module. Notably, the protrusion 243 may contain other sensorsthat benefit from close proximity and/or secure contact to the user'sskin. These may be included in addition to or in lieu of a heart ratesensor and may include sensors such as a skin temperature sensor (e.g.,noncontact thermopile that utilizes the optical window or thermistorjoined with thermal epoxy to the outer surface of the protrusion), pulseoximeter, blood pressure sensor, EMG, or galvanic skin response (GSR)sensor.

Collecting Physiological and/or Environmental Information

The wearable computing device 201 may be configured to collect one ormore types of physiological and/or environmental data from embeddedsensors and/or external devices and communicate or relay suchinformation to other devices, including devices capable of serving as anInternet-accessible data sources, thus permitting the collected data tobe viewed, for example, using a web browser or network-basedapplication. For example, while the user is wearing the wearablecomputing device 201, the wearable computing device 201 may calculateand store the user's step count using one or more biometric sensors. Thewearable computing device 201 may then transmit data representative ofthe user's step count to an account on a web service, computer, mobilephone, or health station where the data may be stored, processed, andvisualized by the user. Indeed, the wearable computing device 201 maymeasure or calculate a plurality of other physiological metrics inaddition to, or in place of, the user's step count. These include, butare not limited to, energy expenditure, e.g., calorie burn, floorsclimbed and/or descended, heart rate, heart rate variability, heart raterecovery, location and/or heading, e.g., through GPS or a similarsystem, elevation, ambulatory speed and/or distance traveled, swimminglap count, swimming stroke type and count detected, bicycle distanceand/or speed, blood pressure, blood glucose, skin conduction, skinand/or body temperature, muscle state measured via electromyography,brain activity as measured by electroencephalography, weight, body fat,caloric intake, nutritional intake from food, medication intake, sleepperiods, e.g., clock time, sleep phases, sleep quality and/or duration,pH levels, hydration levels, respiration rate, and other physiologicalmetrics.

The wearable computing device 201 may also measure or calculate metricsrelated to the environment around the user such as barometric pressure,weather conditions (e.g., temperature, humidity, pollen count, airquality, rain/snow conditions, wind speed), light exposure (e.g.,ambient light, UV light exposure, time and/or duration spent indarkness), noise exposure, radiation exposure, and magnetic field.Furthermore, the wearable computing device 201 or the system collatingthe data streams from the wearable computing device 201 may calculatemetrics derived from such data. For example, the device or system maycalculate the user's stress and/or relaxation levels through acombination of heart rate variability, skin conduction, noise pollution,and body temperature. Similarly, the wearable computing device 201 orthe system collating the data streams from the wearable computing device201 may determine values corresponding to sleep quality metrics derivedfrom such data. For example, the device or system may calculate theuser's restlessness and/or quality of rapid-eye movement (REM) sleepthrough a combination of heart rate variability, blood oxygen level,sleep duration, and body temperature. In another example, the wearablecomputing device 201 may determine the efficacy of a medicalintervention, e.g., medication, through the combination of medicationintake, sleep data, and/or activity data. In yet another example, thebiometric monitoring device or system may determine the efficacy of anallergy medication through the combination of pollen data, medicationintake, sleep and/or activity data. These examples are provided forillustration only and are not intended to be limiting or exhaustive.Further embodiments and implementations of sensor devices may be foundin U.S. Pat. No. 9,167,991, titled “Portable Biometric MonitoringDevices and Methods of Operating Same” filed Jun. 8, 2011, which ishereby incorporated herein by reference in its entirety.

Sleep Quality Metrics

As described above, a reliable, yet user-friendly approach is desired inthe field of sleep therapy and sleep quality assessment. FIG. 3 is atable 300 of sleep quality assessment metrics and associatedphysiological data in accordance with one or more embodiments of thepresent disclosure. Table 300 includes a metric column 302 and inputcolumn 304 as well as three rows of types of sleep quality metrics,namely user goal-driven metrics 306, user-normalized metrics 308 andpopulation-normalized metrics 310. In some embodiments, inputs in inputcolumn 304 include directly detected physiological and/or environmentalsensor readings (e.g., instantaneous heart rate), while in someembodiments, inputs in input column 304 include intermediate parametersderived from directly detected physiological and/or environmental sensorreadings (e.g., average heart rate over a period of sleep). Furtherembodiments and implementations of collecting and interpretingphysiological and/or environmental sensor readings for sleep qualityassessment may be found in U.S. patent application Ser. No. 15/438,643,titled “Methods and Systems for Labeling Sleep States” filed Feb. 21,2017, which is hereby incorporated herein by reference in its entirety.

The various sleep quality metrics conveyed in table 300 are eachassociated with a distinct diagnostic basis for sleep quality. Forexample, one aspect of good sleep health is having an adequate totalduration of sleep corresponding to the age, gender and overall health ofa respective user. The first type of sleep quality metric that may beused to determine a unified sleep score, is goal-driven 306. Goal-drivenmetrics 306 may require input from a user to set one or more sleepgoals, and may require feedback from a user regarding perceived qualityof sleep. Goal-driven metrics 306, include, but are not limited to, atotal sleep time metric and a sleep consistency metric.

Determining a value for the total sleep time metric may rely on one ormore inputs, including, but not limited to a user-specified minimumtarget duration of sleep goal, a maximum target duration of sleep goaland/or a target range of sleep goal. For example, a biometric monitoringdevice performing a sleep quality assessment, may prompt a user to entera goal for achieving a duration of sleep for a given night (e.g., sleepfor at least 7 hours tonight), or to enter a goal for achieving aduration of sleep every night (e.g., sleep for 7-8 hours every night).Determining a value for the total sleep time metric may also rely on adetermined value of total sleep duration achieved by the user for arespective period of sleep. For example, a wearable computing device maydetect that a user fell asleep at 11 PM on Monday night, and woke up at6 AM on Tuesday morning, for a total sleep duration of 7 hours. In someembodiments, the onset of sleep and the onset of wakefulness aredetected by one or more physiological sensors. Determining a value forthe total sleep time metric may use the user-specified sleep goal alongwith the detected total sleep duration for a respective period of sleep(e.g., for a given night), and generate a value corresponding to a levelof achievement for reaching the user-specified goal. For example, if theuser aimed to sleep for at least 7 hours, and had a total sleep durationfor 7.5 hours, the total sleep time metric would have a maximum value(e.g., 10 out of 10 or 100%) for a given range of possible values.

Another of the possible goal-driven metrics 306, is sleep consistency.In some implementations, a value of a sleep consistency metric relies onfeedback obtained from a user after waking from a period of sleep. Forexample, a biometric monitoring device may detect that a user has awokenfrom a night of sleep and prompt the user to provide a self-assessmentof perceived quality of sleep or feeling of restfulness. This feedbackmay be in a numerical format (e.g., 7 out of 10) or a subjective form,which is converted into a numerical value (e.g., good=1, neutral=0,bad=−1).

The second type of sleep quality metric that may be used to determine aunified sleep score, is user-normalized 308. User-normalized metrics 308are specific to a respective user undergoing sleep assessment, and relyon historical physiological information about the user. In certainembodiments, determining a respective value for a respectiveuser-normalized metric 308 may require at least M days worth of detectedphysiological and/or environmental data (e.g., at least 7 days). In someembodiments, determining a respective value of a respectiveuser-normalized metric 308 may use up to a maximum N days worth of themost recently detected physiological and/or environmental data (e.g., atmost the last 30 days). User-normalized metrics 308 may include, but notbe limited to, a restlessness metric, a long wakes metric and a heartrate metric.

In some implementations, a value of a restlessness metric is determinedusing a value for detected movement of a user during a period of sleep.A degree of movement may be based on a duration of detected movementwhile a user is asleep. For example, a computing device worn by a userwhile asleep detects that the user remained nearly motionless for 6 outof 7 hours of sleep, which may indicate a low degree of movement duringthe period of sleep. A degree of movement may be based on a severity ofdetected movement while a user is asleep. For example, if a user has abad dream and moves with great force or great speed, this movement mayindicate a high degree of movement during the period of sleep. A valueof a restlessness metric may also rely on a value of detected totalsleep duration. For example, if 15 minutes of movement were detectedduring an 8 hour period of sleep, this would indicate less overallrestlessness than detecting 15 minutes of movement during a 5 hourperiod of sleep. A value or weighting of the value of the restlessnessmetric may also involve comparing a current assessment of restlessnessto the user's historical values of the restlessness metric. For example,if the user typically has a relatively calm night of sleep with littlemovement, a night with a high restlessness score has a relatively highstandard deviation from the normal values and may be weighted morenegatively than if the user typically has restless sleep. Similarly, inthis same example, if the user typically has a restless night of sleep,a night with a low restlessness score may be weighted more positivelythan for a user who typically has calm nights of sleep.

In some implementations, a value of a long wakes metric is determinedusing a value representing a detected period of wakefulness or totalduration of time between periods of sleep. For example, if a user getsup out of bed multiple times in a given night, the value of the longwakes metric may be low, indicating that the user did not have a long,continuous period of sleep. Similarly to the restlessness metric,determining the value or a weighting of the value of the long wakesmetric may involve comparing the value of the determined long wakesmetric to historical values of the long wakes metric for a respectiveuser.

In some implementations, a value of a heart rate metric is determinedusing one or more heart rate parameters. Determining a value for theheart rate metric may include using a value representing the averageheart rate of a user during a period of sleep (e.g., 45 beats perminute). For example, if the user had an average sleeping heart rateabove a particular threshold (e.g., 60 beats per minute), this mayresult in a low value for the heart rate metric. The average sleepingheart rate of a user may also be compared to historical average sleepingheart rates of the user for a number of days, to assess if the currentlydetermined average sleeping heart rate is abnormally high or low for theuser.

In some implementations, the value of a heart rate metric includesassessment of a wakeful resting heart rate of the user during a longduration of wakefulness before detecting that the user is attempting tosleep (e.g., during the day before a night's sleep). For example, a usermay have had an average resting heart rate of 55 beats per minute duringthe day, and an average sleeping heart rate of 53 beats per minute. Thisunusually small difference between the average resting heart rate of theuser during the day and the average sleeping heart rate of the userwhile asleep may result in a low value for the heart rate metric,indicating that the user may not have had a good night of sleep. In someimplementations, detection of a sleeping heart rate (e.g., average,median, or instantaneous) value above a wakeful resting heart rate valueis an indication of poor quality sleep. As a result, the heart ratemetric may be assigned a low value and/or the weight of the heart ratemetric value in determining a population-normalized score and/or theunified sleep score may be increased.

The use of a wakeful resting heart rate of the user may also indicate amental or physical health condition (e.g., a cold, or anxiety), whichmay affect detected and/or perceived sleep quality. For example, if auser has a relatively high resting heart rate for several days or weeksin a row, this may correspond to a relatively high sleeping heart ratefor the same several days or weeks. As a result, weighting of the valueof an average sleeping heart rate may be lessened to compensate for thischange in physical and/or mental health of the user. While the examplesof a detected heart rate have referred to averaged values, in someimplementations another statistical basis may be used to assess arespective heart rate of a user. For example, an entire heart ratepattern may be analyzed for a given period of time (e.g., total durationof sleep). A median heart rate value may be used instead of an averageheart rate value for a given heart rate parameter. Extreme values forheart rates over a period of time may also be filtered out beforeperforming a statistical operation to determine a representative valuefor a respective type of heart rate.

The third type of sleep quality metric that may be used to determine aunified sleep score, is population-normalized 310. Population-normalizedmetrics 308 are sleep quality metrics assessed with respect to aplurality of users. In certain embodiments, determining a respectivevalue for a respective population-normalized metric 310 includescomparing determined values for a user to those of other users of asimilar demographic (e.g., same gender, same age range, sameoccupation). Population-normalized metrics 310 may include, but not belimited to, a deep sleep metric, a rapid-eye movement (REM) metric, awake after sleep onset (WASO) metric and a breathing disturbancesmetric. Population-normalized metrics may be associated with a pluralityof users of the sleep quality assessment system described herein, and/orwith clinical data obtained from sleep therapy resources such as sleeplaboratory readings of various patients.

In some implementations, a value of a deep sleep metric is determinedusing a determined value of a duration of time that a user experiencesdeep sleep during a total duration of sleep. In some embodiments, atotal duration of non-rapid eye movement (NREM) sleep is used fordetermining the value of the deep sleep metric, while in someembodiments, a total duration of deep sleep is a subset of time during atotal duration of NREM sleep. For example, a total duration of deepsleep is determined to include time when brain waves of a user have afrequency of less than 1 Hz. Deep sleep is physiologically linked to theconsolidation of new memories, and physical and mental recovery. As aresult, a relatively low value of duration of deep sleep, as compared toa plurality of users, may result in a low value for the deep sleepmetric.

In some implementations, a value of a rapid-eye movement (REM) sleepmetric is determined using a value of a duration of time that a userexperiences REM sleep during a total duration of sleep. In someimplementations, duration of REM sleep and/or NREM sleep and/or deepsleep is determined on the basis of detected movement of a user, heartrate, breathing patterns, brain activity and/or body temperature. REMsleep deprivation is linked to mental and physical health issues,therefore a relatively low value of duration of REM sleep, as comparedto a plurality of users, may result in a low value for the REM sleepmetric.

In some implementations, a value of a wake after sleep onset (WASO)metric is determined using a value of total or average time betweenperiods of sleep that a user experiences during a total duration ofsleep. For example, a user may wake up three times at night due tonightmares or environmental disturbances, and have a total value of 45minutes of time between periods of sleep. Continuous sleep is linked toimprovements in duration of REM and deep sleep, therefore, a relativelyhigh value of total or average time between periods of sleep as comparedto a plurality of users, may result in a low value for a WASO metric.

In some implementations, a value of a breathing disturbances metric isdetermined using a value of a detected duration of halted breathing thata user experiences during a total duration of sleep. For example, awearable computing device worn by a user while asleep, may detect apulse oximeter reading below 90%, indicating a low blood oxygen leveland suggesting that the user has stopped breathing. In this example, theuser may wake up, start breathing again, and have a pulse oximeterreading greater than 90% or another threshold value for some time. Whilea pulse oximeter is one example of a physiological sensor to determinebreathing disturbances, measurement of a duration of halted breathing bya user during sleep, is not limited to this example. Duration of haltedbreathing may also be detected by brain activity, for example. Thresholdlevels of blood oxygen levels, brain activity and/or breathing activityfor determining a high level of breathing disturbances, may bedetermined by a plurality of users.

While table 300 portrays several sleep quality metrics and several typesof sleep quality metrics that may be utilized by a biometric monitoringdevice to determined a unified sleep quality score, it should beunderstood that additional sleep quality metrics may be used.Furthermore, in some implementations a subset of the sleep qualitymetrics shown in table 300 are used to determined a sleep quality score.For example, a wearable computing device that does not have a sensor todetermine breathing disturbances does not use the breathing disturbancesmetric to determine a unified sleep quality score for purposes of sleepquality assessment.

Generation of Unified Sleep Quality Score

FIG. 4A is a block diagram 400 of a basis of determining a unified sleepscore in accordance with one or more embodiments. In someimplementations, one or more goal-driven metrics are used along with oneor more user normalized metrics, each as described above with respect toFIG. 3, to determine an overall user normalized score. In someimplementations, goal-driven metrics are not used to determine anoverall user normalized score. In some implementations, populationnormalized metrics, as described above with respect to FIG. 3, are usedto determine an overall population normalized score.

The determination of the user normalized score and the populationnormalized score depends on which specific sleep quality metrics areused and the weight of each selected sleep quality metric in thecalculation of the each score. For example, a total sleep time,goal-driven sleep quality metric, may be given a relatively high weightin determining the user normalized score, while a restlessness metricmay be given a relatively low weight, or determined not to be used. Insome implementations, the selection of sleep metrics used to determinethe user normalized and/or population normalized score depends on thecapability of a biometric monitoring device used to perform sleepassessment of a user. For example, if a user wears a computing devicethat cannot measure a heart rate, the heart rate metric is not used todetermine the user normalized score.

The weighting basis for sleep metrics used to determine the usernormalized score and/or the population normalized score may changeautomatically or manually. For example, an atypical value for arespective sleep quality metric may result in a higher than defaultweighting for that respective sleep quality metric, or may limit theupper or lower bound of an associated score. The weighting basis mayalso change over time as a user develops better sleep habits, in orderto motivate the user to continually achieve better sleep quality. Theweighting basis may adjust from a default basis after a period ofcollecting physiological and/or environmental data while a user sleepsfor a minimum threshold number of total sleep periods (e.g., nights). Auser may also be able to adjust the weighting basis for one or moresleep quality metrics, in determining an overall unified sleep qualityscore, which relies on the various goal-driven, user normalized and/orpopulation normalized metrics. For example, a user may be able toindicate a strong link to restlessness and perceived sleep quality,resulting in an increased weighting of that metric. In someimplementations, one or more population-normalized metrics are givenmore weight than one or more user-normalized metrics, while in someimplementations one or more user-normalized metrics are given moreweight than one or more population-normalized metrics.

After determining a respective user normalized score and a respectivepopulation normalized score for sleep quality, a unified sleep qualityscore is determined, using the user normalized score and the populationnormalized score. In some implementations, there is a weightingcomponent of each of the user normalized score and the populationnormalized score in the determination of the unified sleep score. Thisweighting component may be adjusted manually or automatically, asdescribed above with respect to the weighting basis for each of the usernormalized and population normalized scores.

FIG. 4B is a table 402 of a basis of determining a unified sleep scorein accordance with one or more embodiments. The examples of table 402are non-limiting, and are merely used to illustrate one particulartechnique for using a user normalized score and a population normalizedscore to generate a unified sleep quality score. As described withrespect to FIGS. 3 and 4A, a user normalized score may be based on oneor more of a heart rate metric, a total sleep time metric, a sleepconsistency metric, a restlessness metric, and a time between sleepperiods metric. It should be understood that this is a non-limitingscoring basis for generating the user normalized score, and that a usernormalized score may be based on additional user-specific sleep qualitymetrics not described herein. As described with respect to FIGS. 3 and4A, a population normalized score may be based on one or more of a deepsleep metric, a REM metric, a WASO metric and a breathing disturbancesmetric. It should be understood that this is a non-limiting scoringbasis for generating the population normalized score, and that apopulation normalized score may be based on additional populationnormalized sleep quality metrics not described herein.

A user normalized score may be determined and converted into a numericalvalue on a preset scale (e.g., −4 to 1, or 0 to 4, or 1 to 100).Similarly, a population normalized score may be determined and convertedinto a numerical value on a preset scale. The non-limiting examplesshown in table 402 illustrate a range of values from −4 at the worst to1 at the best for a user normalized score, and from −1 at the worst to 1at the best for a population normalized score. In some embodiments, theuser normalized score and the population normalized score use the samescoring value range. The non-limiting example calculation of a unifiedsleep quality score combines the determined user normalized score andpopulation normalized score, multiplies the sum by 10, and adds theresult to 80 to obtain a final value ranging from 30 at the worst to 100at the best.

Sleep Quality Assessment System

In certain embodiments described in the present disclosure, a wearablecomputing device is capable of, and configured to collect physiologicalsensor readings of a user, and determine values of one or more sleepquality metrics and/or a unified sleep score. However, in someembodiments, a wearable computing device, or another portable electronicdevice used to detect physiological information of a user, is incommunication with another computing device configured to determinethese values. FIG. 5 illustrates a networked relationship 500 between awearable computing device 502 and an external computing device 504 inaccordance with one or more embodiments.

The wearable computing device 502 may be configured to collect one ormore types of physiological and/or environmental data from embeddedsensors and/or external devices, as described throughout the presentdisclosure, and communicate or relay such information over one or morenetworks 506 to other devices. This includes, in some implementations,relaying information to devices capable of serving asInternet-accessible data sources, thus permitting the collected data tobe viewed, for example, using a web browser or network-based applicationat an external computing device 504. For example, while the user isasleep and wearing the wearable computing device 502, the wearablecomputing device 502 may calculate and optionally store the user'ssleeping heart rate using one or more biometric sensors. The wearablecomputing device 502 may then transmit data representative of the user'ssleeping heart rate over network 506 to an account on a web service,computer, mobile phone, or health station where the data may be stored,processed, and visualized by the user.

While the example wearable computing device 502 is shown to have atouchscreen display, it should be understood that the wearable computingdevice 502 may not have any type of display unit, or may have anyvariety of audio and/or visual feedback components, such aslight-emitting diodes (LEDs) (of various colors), buzzers, speakers, ora display with limited functionality. The wearable computing device 502may be configured to be attached to the user's body, or clothing, suchas a wrist bracelet, watch, ring, electrode, finger-clip, toe-clip,chest-strap, ankle strap or a device placed in a pocket. Additionally,the wearable computing device 502 may alternatively be embedded insomething in contact with the user such as clothing, a mat under theuser, a blanket, a pillow or another accessory involved in the activityof engaging in sleep.

The communication between wearable computing device 502 and externaldevice 504 may be facilitated by one or more networks 506. In someimplementations, one or more networks 506 include one or more of an adhoc network, a peer to peer communication link, an intranet, anextranet, a virtual private network (VPN), a local area network (LAN), awireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), ametropolitan area network (MAN), a portion of the Internet, a portion ofthe Public Switched Telephone Network (PSTN), a cellular telephonenetwork, or any other type of network. The communication between thewearable computing device 502 and external device 504 may also beperformed through a direct wired connection. This direct-wiredconnection may be associated with any suitable or desirablecommunication protocol and/or physical connector, such as UniversalSerial Bus (USB), Micro-USB, WiFi, Bluetooth, FireWire, PCIe, or thelike.

A variety of computing devices may be in communication with the wearablecomputing device 502, to facilitate sleep quality assessment. Asdepicted in FIG. 5, an example external computing device 504 is a smartphone with a display 508. An external computing device 504 may be anycomputing device capable of assessing values for one or more sleepquality metrics and/or a unified sleep score, such as, but not limitedto, a smart phone, personal digital assistant (PDA), a mobile phone, atablet, a personal computer, a laptop computer, a smart television,and/or a video game console. This example external computing device 504illustrates that in some embodiments, through this networkedrelationship shown in FIG. 5, an external computing device 504 may beimplemented to determine one or more sleep quality metrics, and/or aunified sleep score. For example, a user wears a wearable computingdevice 502 equipped as a bracelet with one or more physiological sensorsbut without a display. In this example, over the course of the night, asthe user sleeps, the wearable computing device 502 records the user'sheart beat, movement, body temperature and blood oxygen level as well asroom temperature and ambient light levels, and periodically transmitsthis information to the external computing device 504. Alternatively,the wearable computing device 502 may store and transmit the collectedphysiological and/or environmental data and transmit this data to theexternal computing device 504 in response to a trigger, such asdetection of the user being awake after a period of being asleep. Insome implementations, the user must be awake for a threshold period oftime to set this trigger (e.g., awake for at least 10 minutes), and/orawake for a threshold period of time after a threshold period of sleephas been experienced (e.g., awake for at least 5 minutes after having atleast 6 hours of sleep). In some implementations, a trigger fordetermining one or more values of sleep quality metrics and/or a unifiedsleep score, is detection of a command performed at the externalcomputing device 504, such as manual or automatic execution of aninstruction to synchronize collected physiological and/or environmentaldata and determine the unified sleep score.

The example external computing device 504 illustrates that in someimplementations, the external computing device 504 may present thevalues of one or more sleep quality metrics and/or a unified sleepscore. This presentation may be made to the user (e.g., wearer) of thewearable computing device 502, or may, for example, be made to a sleeptherapy provider for the user, such as a doctor or caregiver. In someimplementations, the external computing device 504 determines one ormore values for one or more sleep quality metrics and/or a unified sleepscore and sends this information back to the wearable computing device502 via the one or more networks 506, for presentation of suchdetermined value or values to the user (e.g., wearer) of the wearablecomputing device. Additional information regarding presentation of aunified sleep score and/or values of one or more sleep quality metricsare discussed below, with respect to FIGS. 7A and 7B.

FIG. 6 illustrates a system 600 of client devices and a server system608 for performing sleep quality assessment in accordance with one ormore embodiments. In certain implementations, a sleep quality assessmentsystem or platform, is implemented across a plurality of electronicdevices. The wearable computing devices 502 a, 502 b and 502 c may havecharacteristics similar to those described above with respect towearable computing device 502 in FIG. 5, where each wearable computingdevice is coupled to a respective user 602 a, 602 b and 602 c. The oneor more networks 506 may have characteristics similar to those describedabove with respect to one or more networks 506 in FIG. 5. The exampleexternal computing devices 504 a (e.g., a laptop computer), 504 b (e.g.,a smart phone), 504 c (e.g., a personal computer) may havecharacteristics similar to those described above with respect toexternal computing device 504 in FIG. 5.

In some implementations, of a sleep quality assessment system 600, oneor more wearable computing devices such as wearable computing device 502a are directly connected to one or more networks 506, connecting serversystem 608, and optionally connecting one or more external computingdevices such as external computing devices 504 a, 504 b, and/or 504 c.In some implementations, one or more external computing devices 504 a,504 b and/or 504 c are interconnected in a local area network (LAN) 604(or another type of communication interconnection), which is connectedto the one or more networks 506. The example LAN 604 may interconnectone or more external computing devices such as devices 504 a, 504 b, or504 c, as well as one or more wearable computing devices such as 502 c.In some implementations, one or more wearable computing devices such aswearable computing device 502 b are connected to one or more networks506, indirectly, through LAN 604 to one or more external computingdevices 504, which is/are connected to the one or more networks 506. Insome implementations, one or more wearable computing devices such aswearable computing device 502 b are connected to one or more networks506 directly, and indirectly through LAN 604, as described above. Forexample, wearable computing device 502 b is connected to smart phone 504b through a Bluetooth connection, smart phone 504 b is connected toserver system 608 through network 506, and wearable computing device 502b is also connected to server system 608 through network 506.

Sleep Quality Assessment Server

A sleep quality assessment system 600 may implement a server system 608to collect detected physiological and/or environmental sensor readingsfrom one or more wearable computing devices such as devices 502 a, 502 band 502 c as shown. In some implementations, server system 608 may alsocollect values of sleep quality assessment metrics and/or a unifiedsleep score from one or more wearable computing devices such as devices502 a, 502 b and 502 c and/or from one or more external computingdevices such as devices 504 a, 504 b and 504 c as shown. For example,wearable computing device 502 a is not associated with an externalcomputing device, therefore it transmits collected physiological datawhile user 602 a sleeps to server system 608, which analyzes thereceived data to determine values of one or more sleep quality metricsand a unified sleep score, to transmit back to wearable computing device502 a. In another example, wearable computing device 502 b transmitscollected physiological data while user 602 b sleeps (or has finishedsleeping) to both server system 608 and external computing device 504 a.In this example, external computing device 504 a determines values forone or more sleep quality metrics and a unified sleep score, whileserver system 608 uses the received physiological data to update a userprofile for user 602 b, stored in profiles database 614.

In some implementations, server system 608 is implemented on one or morestandalone data processing apparatuses or a distributed network ofcomputers. In some embodiments, server system 108 also employs variousvirtual devices and/or services of third party service providers (e.g.,third-party cloud service providers) to provide the underlying computingresources and/or infrastructure resources of server system 108. In someembodiments, server system 108 includes, but is not limited to, ahandheld computer, a tablet computer, a laptop computer, a desktopcomputer, or a combination of any two or more of these data processingdevices or other data processing devices.

Server system 608 may include one or more processors or processing units610 (e.g., CPUs) and one or more network interfaces 618 including an I/Ointerface to external computing devices and wearable computing devices.In some implementations, server system 608 includes memory 612, and oneor more communication buses for interconnecting these components. Memory612 includes high-speed random access memory, such as DRAM, SRAM, DDRRAM, or other random access solid state memory devices; and, optionally,includes non-volatile memory, such as one or more magnetic disk storagedevices, one or more optical disk storage devices, one or more flashmemory devices, or one or more other non-volatile solid state storagedevices. Memory 612, optionally, includes one or more storage devicesremotely located from one or more processing units 610. Memory 612, oralternatively the non-volatile memory within memory 612, includes anon-transitory computer readable storage medium. In someimplementations, memory 612, or the non-transitory computer readablestorage medium of memory 612, stores one or more programs, modules, anddata structures. These programs, modules and data structures mayinclude, but not be limited to one or more of an operating systemincluding procedures for handling various basic system services and forperforming hardware dependent tasks, a network communication module forconnecting server system 608 to other computing devices (e.g., wearablecomputing devices 502 a, 502 b and 502 c and/or external computingdevices 504 a, 504 b and 504 c) connected to one or more networks 506via one or more network interfaces 618 (wired or wireless).

Memory 612 may also include a unified score determination module 614 forusing collected physiological and/or environmental data of one or moreusers (e.g., received from one or more wearable computing devices orexternal computing devices), to determine values of one or more sleepquality metrics and/or a unified sleep score, as described earlier inthe present disclosure, with respect to FIGS. 3, 4A and 4B. Memory 612may also include a profiles database 616 storing user profiles for usersof the sleep quality assessment system 600, where a respective userprofile for a user may include a user identifier (e.g., an account nameor handle), login credentials to the sleep quality assessment system,email address or preferred contact information, wearable computingdevice information (e.g., model number), demographic parameters for theuser (e.g., age, gender, occupation, etc.), historical sleep qualityinformation and identified sleep quality trends of the user (e.g.,particularly restless sleeper). In some implementations, collectedphysiological information of a plurality of users (such as users 602 a,602 b and 602 c) of the sleep quality assessment system 600 provides formore robust population-normalized sleep metrics, as described above withrespect to FIGS. 3, 4A and 4B. For example, user 602 a is a 35 year oldfemale veterinarian and user 602 b is a 34 year old female veterinarian,and each of their respective historical sleep quality physiological dataand/or metrics are used in the determination of one or morepopulation-normalized sleep quality metrics for each other, due to theirclosely aligned demographic characteristics. In some implementations, auser may opt in or opt out of providing sleep quality assessmentinformation to a population-normalization determination for other users.In some implementations, a user's sleep quality information may beincorporated into population-normalized sleep quality metric informationused to determine that user's own values for one or more sleep qualitymetrics.

Presentation of Unified Sleep Quality Score

FIG. 7A illustrates embodiments of wearable computing devices 700 a and700 b, each having displays for presenting a representation of a unifiedsleep score in accordance with one or more embodiments. The computingdevices 700 a and 700 b of FIG. 7A each illustrate a generallyrectangular display. Computing device 700 a illustrates that somewearable computing devices may have a set of light-emitting diodes(LEDs), to indicate a status, or in this context, a representation of aunified sleep quality score. For example, 3 out of 4 illuminated LEDs,as shown in display 702 a, may indicate a moderately good unified sleepquality score.

In certain embodiments, computing device 700 b may be configured todisplay text and/or other visual elements on the display 702 b. Thedisplay 702 b of the computing device 700 b may represent a relativelysmall display, wherein portrayal of extensive information may beundesirable and/or impractical. As a result, display 702 b of a wearablecomputing device 700 b with limited display capability, may simplyportray a numerical (e.g., 45, or 8/10) and/or subjective representation(e.g., good, bad, neutral) of the unified sleep quality score.

FIG. 7B illustrates embodiments of wearable computing devices havingtouchscreen displays for presenting a representation of a unified sleepscore in accordance with one or more embodiments. FIG. 7B illustrateswearable computing devices 704 a and 704 b having a generallyrectangular, horizontally-arranged, display 706 a and 706 b,respectively. Display 706 a illustrates that in some implementations, aunified sleep quality score may be portrayed along with more detailedinformation about the sleep quality metrics contributing to the unifiedsleep quality score. In some implementations, a user may be able toscroll through this detailed information on display 706 a. Display 706 billustrates that a representation of a unified sleep quality score maybe in the form of a graphic, emoji, background color or anotheraudio-visual representation other than numbers or text.

Methods for Assessing Sleep Quality

FIG. 8 illustrates a flow diagram for a process 800 for determining asleep quality assessment in accordance with the present disclosure. Incertain embodiments, the process 800 may be performed at least in partby a computing device having one or more physiological and/orenvironmental sensors and/or control circuitry of the computing device.For example, a user may be wearing a computing device on a wrist orother body part, or otherwise have the computing device attached to himor her, when at least part of the process 800 is performed. The process800 may be performed in order to assess the quality of sleep experiencedby a user, using one or more sleep quality metrics based off of detectedphysiological and/or environmental information. In some implementations,process 800 is performed at least in part by a plurality of electronicdevices (e.g., a wearable computing device and an external computingdevice), and in some implementations, process 800 is performed at leastin part by a sleep quality assessment server (e.g., server system 608 asdescribed with respect to FIG. 6 and a wearable computing device).

In certain embodiments, prior to execution of the first step 802 of theprocess 800, a user may have been detected to be in a wakeful state. Forexample, the user is detected to be awake and not attempting to sleepfrom 7:01 AM until 10:59 PM. When the user attempts to fall asleep, oneor more measurable physiological parameters relating to sleep qualitymetrics may be triggered to be detected. In certain embodiments, theprocess 800 may be implemented in connection with a wearable computingdevice that may be worn, for example, about a wrist or other member ofthe user. In certain embodiments, the sleep quality assessment process800 may be initiated when the user puts on the device and/or contacts adevice with the user's skin and/or lays down in a reclined position.Alternatively, sleep quality assessment processes in accordance with thepresent disclosure may be initiated after a predetermined period of timein an idle state, or in connection with a request to initiate a sleepassessment program.

At block 802, the process 800 involves detecting an attempt by a user tofall asleep, or receiving one or more signals indicating an attempt bythe user to fall asleep. For example, a user is wearing a computingdevice that detects that the user has entered a reclined position, thatthe ambient light is low and/or that the device is in contact with theuser but is relatively motionless. In some implementations, process 800includes detecting an onset of sleep by the user, or receiving one ormore signals indicating an onset of sleep by the user and determining aduration of time from the attempt to fall asleep to the onset of sleep.In some implementations, sleep attempt detection is based automaticallyoff of one or more physiological and/or environmental sensor readings,while in some implementations it is based off of manual input from theuser (e.g., pressing a button on a wearable computing device orindicating attempt to sleep in an application on a wearable or externalcomputing device).

At block 804, the process 800 involves collecting (or receiving)physiological information associated with the user, including at leastone sleeping heart rate. For example, the sleeping heart rate may be anaverage of periodically measured heart rate values of a user during atotal duration of sleep (e.g., during a night of sleep). In someimplementations, the physiological information is collected periodicallyand/or upon detection of a change in some condition (e.g., movement). Insome implementations, physiological information is still collected whilethe user is awake, such as in a period of time between an attempt tofall sleep and the onset of sleep, or during a period of wakefulnessbefore the user awakens for a long period of time (e.g., waking up inthe morning). In some implementations, collecting physiologicalinformation about the user includes collecting one or more sets ofvalues associated with: movement of the user, total sleep duration,total deep sleep duration, duration of wake time after sleep onset(WASO), total rapid-eye-movement (REM) sleep duration, total light sleepduration, breathing patterns of the user, breathing disturbances of theuser and temperature of the user.

At block 806, the process 800 involves determining respective values forone or more sleep quality metrics, based at least in part on thecollected physiological information. In some implementations,determining respective values for one or more sleep quality metricsincludes using at least one wakeful resting heart rate of the user. Forexample, determining a value for a heart rate metric includes comparingan average sleeping heart rate of the collected physiologicalinformation, and an average wakeful resting heart rate detected andcalculated before the user attempts to fall asleep. In someimplementations, the wakeful resting heart rate is determinedperiodically during a period of extended wakefulness before the userenters a state of sleep. In some implementations, the wakeful restingheart rate is the lowest resting heart rate value detected while theuser is awake. In some implementations, determining respective valuesfor the one or more sleep quality metrics includes comparing the atleast one sleeping heart rate of the user and the at least one wakefulresting heart rate of the user. In some implementations, determiningrespective values for the one or more sleep quality metrics includescomparing the at least one sleeping heart rate of the user to athreshold value.

In certain embodiments, the one or more sleep quality metrics includes afirst set of sleep quality metrics associated with sleep quality of aplurality of users, and a second set of sleep quality metrics associatedwith historical sleep quality of the user. In certain embodiments,process 800 further includes detecting that the user is awake after thedetected onset of sleep, and determining the respective values for oneor more sleep quality metrics, in response to detecting that the user isawake.

At block 808, the process 800 includes determining a unified score forsleep quality of the user, based at least in part on the respectivevalues of the one or more sleep quality metrics. In certain embodiments,determining the unified score for sleep quality includes determining arespective metric-score for each of the one or more sleep qualitymetrics and applying a respective weighting for each of the one or moresleep quality metrics. In some embodiments, process 800 includescollecting sleep quality feedback information from the user anddetermining the unified score for sleep quality of the user based atleast in part on the collected sleep quality feedback information.

At block 810, the process 800 involves presenting a representation ofthe unified score to the user, or generating instructions to provide arepresentation of the unified score to the user. For example, as shownin FIGS. 7A and 7B, a representation of the unified score may varydepending on the audio.visual feedback capability of a wearable and/orbiometric monitoring device. A representation of the unified score mayinclude any of alphanumeric characters, graphics, sounds, lights,patterns and animations. In another example, server system 608 of FIG. 6sends instructions to a wearable computing device to present a unifiedsleep quality assessment score of 85, on a display of the wearablecomputing device.

FIG. 9 illustrates a process 900 for determining a sleep qualityassessment in accordance with one or more embodiments disclosed herein.The process 900 may be performed at least in part by a computing devicehaving one or more physiological and/or environmental sensors and/orcontrol circuitry of the computing device. For example, the user may bewearing a computing device on a wrist or other body part, or otherwisehave the computing device attached to him or her, when performing atleast part of the process 900. The process 900 may be performed in orderto assess the quality of sleep experienced by a user, using one or moresleep quality metrics based off of detected physiological and/orenvironmental information. In some implementations, process 900 isperformed at least in part by a plurality of electronic devices (e.g., awearable computing device and an external computing device), and in someimplementations, process 900 is performed at least in part by a sleepquality assessment server (e.g., server system 608 as described withrespect to FIG. 6 and a wearable computing device).

At block 902, the process 900 involves detecting an attempt by a user tofall asleep or receiving one or more signals indicating an attempt bythe user to fall asleep. For example, a user is wearing a computingdevice that detects that the user has entered a reclined position, thatthe ambient light is low and/or that the device is in contact with theuser but is relatively motionless. In some implementations, process 900includes detecting an onset of sleep by the user, or receiving one ormore signals indicating an onset of sleep by the user and determining aduration of time from the attempt to fall asleep to the onset of sleep.

At block 904, the process 900 involves collecting physiologicalinformation associated with the user. In some implementations,collecting physiological information associated with the user includescollecting one or more sets of values associated with: movement of theuser, total sleep duration, total deep sleep duration, duration of waketime after sleep onset (WASO), total rapid-eye-movement (REM) sleepduration, total light sleep duration, breathing patterns of the user,breathing disturbances of the user and temperature of the user.

At block 906, the process 900 involves determining respective values forone or more sleep quality metrics, based at least in part on thecollected physiological information, wherein the one or more sleepquality metrics includes a first set of sleep quality metrics associatedwith sleep quality of a plurality of users, and a second set of sleepquality metrics associated with historical sleep quality of the user. Insome implementations, determining respective values for one or moresleep quality metrics includes using at least one wakeful resting heartrate of the user. For example, determining a value for a heart ratemetric includes using an average sleeping heart rate of the collectedphysiological information, and an average wakeful resting heart ratedetected and calculated before the user attempts to fall asleep. In someimplementations, determining respective values for the one or more sleepquality metrics includes comparing the at least one sleeping heart rateof the user and the at least one wakeful resting heart rate of the user.In certain embodiments, process 900 further includes detecting that theuser is awake after the detected onset of sleep, or receiving one ormore signals indicating that the user is awake, and determining therespective values for one or more sleep quality metrics, in response todetecting that the user is awake.

At block 908, the process 900 includes determining a unified score forsleep quality of the user, based at least in part on the respectivevalues of the one or more sleep quality metrics. In certain embodiments,determining the unified score for sleep quality includes determining arespective metric-score for each of the one or more sleep qualitymetrics and based at least in part on a respective weighting for each ofthe one or more sleep quality metrics. In some embodiments, process 900includes collecting sleep quality feedback information from the user orreceiving collected sleep quality information and determining theunified score for sleep quality of the user based at least in part onthe collected sleep quality feedback information

At block 910, the process 900 involves presenting a representation ofthe unified score to the user or generating a representation of theunified score to present to the user. For example, as shown in FIGS. 7Aand 7B, a representation of the unified score may vary depending on theaudio.visual feedback capability of a wearable and/or biometricmonitoring device. A representation of the unified score may include anyof alphanumeric characters, graphics, sounds, lights, patterns andanimations. In another example, server system 608 of FIG. 6 sendsinstructions to a wearable computing device to present a unified sleepquality assessment score of 85, on a display of the wearable computingdevice.

Additional Embodiments

Depending on the embodiment, certain acts, events, or functions of anyof the processes or algorithms described herein can be performed in adifferent sequence, may be added, merged, or left out altogether. Thus,in certain embodiments, not all described acts or events are necessaryfor the practice of the processes. Moreover, in certain embodiments,acts or events may be performed concurrently, e.g., throughmulti-threaded processing, interrupt processing, or via multipleprocessors or processor cores, rather than sequentially.

Certain methods and/or processes described herein may be embodied in,and partially or fully automated via, software code modules executed byone or more general and/or special purpose computers. The word “module”refers to logic embodied in hardware and/or firmware, or to a collectionof software instructions, possibly having entry and exit points, writtenin a programming language, such as, for example, C or C++. A softwaremodule may be compiled and linked into an executable program, installedin a dynamically linked library, or may be written in an interpretedprogramming language such as, for example, BASIC, Perl, or Python. Itwill be appreciated that software modules may be callable from othermodules or from themselves, and/or may be invoked in response todetected events or interrupts. Software instructions may be embedded infirmware, such as an erasable programmable read-only memory (EPROM). Itwill be further appreciated that hardware modules may be comprised ofconnected logic units, such as gates and flip-flops, and/or may becomprised of programmable units, such as programmable gate arrays,application specific integrated circuits, and/or processors. The modulesdescribed herein are preferably implemented as software modules, but maybe represented in hardware and/or firmware. Moreover, although in someembodiments a module may be separately compiled, in other embodiments amodule may represent a subset of instructions of a separately compiledprogram, and may not have an interface available to other logicalprogram units.

In certain embodiments, code modules may be implemented and/or stored inany type of computer-readable medium or other computer storage device.In some systems, data (and/or metadata) input to the system, datagenerated by the system, and/or data used by the system can be stored inany type of computer data repository, such as a relational databaseand/or flat file system. Any of the systems, methods, and processesdescribed herein may include an interface configured to permitinteraction with patients, health care practitioners, administrators,other systems, components, programs, and so forth.

Conditional language used herein, such as, among others, “can,” “could,”“might,” “may,” “e.g.,” and the like, unless specifically statedotherwise, or otherwise understood within the context as used, isintended in its ordinary sense and is generally intended to convey thatcertain embodiments include, while other embodiments do not include,certain features, elements and/or steps. Thus, such conditional languageis not generally intended to imply that features, elements and/or stepsare in any way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or withoutauthor input or prompting, whether these features, elements and/or stepsare included or are to be performed in any particular embodiment. Theterms “comprising,” “including,” “having,” and the like are synonymous,are used in their ordinary sense, and are used inclusively, in anopen-ended fashion, and do not exclude additional elements, features,acts, operations, and so forth. Also, the term “or” is used in itsinclusive sense (and not in its exclusive sense) so that when used, forexample, to connect a list of elements, the term “or” means one, some,or all of the elements in the list. Conjunctive language such as thephrase “at least one of X, Y and Z,” unless specifically statedotherwise, is understood with the context as used in general to conveythat an item, term, element, etc. may be either X, Y or Z. Thus, suchconjunctive language is not generally intended to imply that certainembodiments require at least one of X, at least one of Y and at leastone of Z to each be present.

Reference throughout this specification to “certain embodiments” or “anembodiment” means that a particular feature, structure or characteristicdescribed in connection with the embodiment is included in at least someembodiments. Thus, appearances of the phrases “in some embodiments” or“in an embodiment” in various places throughout this specification arenot necessarily all referring to the same embodiment and may refer toone or more of the same or different embodiments. Furthermore, theparticular features, structures or characteristics can be combined inany suitable manner, as would be apparent to one of ordinary skill inthe art from this disclosure, in one or more embodiments.

It should be appreciated that in the above description of embodiments,various features are sometimes grouped together in a single embodiment,figure, or description thereof for the purpose of streamlining thedisclosure and aiding in the understanding of one or more of the variousinventive aspects. This method of disclosure, however, is not to beinterpreted as reflecting an intention that any claim require morefeatures than are expressly recited in that claim. Moreover, anycomponents, features, or steps illustrated and/or described in aparticular embodiment herein can be applied to or used with any otherembodiment(s). Further, no component, feature, step, or group ofcomponents, features, or steps are necessary or indispensable for eachembodiment. Thus, it is intended that the scope of the inventions hereindisclosed and claimed below should not be limited by the particularembodiments described above, but should be determined only by a fairreading of the claims that follow.

What is claimed is:
 1. A system, comprising: at least one physiologicalsensor operable to capture one or more signals, the one or more signalsincluding signals representative of movement of an individual; and acomputing device including one or more processors, control circuitry,and a display, the computing device configured to: detect a lack ofmovement, for a threshold period of time, by the individual; collect,using the at least one physiological sensor, physiological data of theindividual; determine respective values for one or more sleep qualitymetrics, based at least in part on the collected physiological data; andpresent, on the display, a representation of a score for sleep quality,the score for sleep quality based at least in part on the respectivevalues of the one or more sleep quality metrics.
 2. The system of claim1, wherein the one or more sleep quality metrics includes a first set ofsleep quality metrics associated with sleep quality of a plurality ofindividuals, and a second set of sleep quality metrics associated withhistorical sleep quality of the individual.
 3. The system of claim 1,wherein the computing device is further configured to: determine arespective metric-score for each of the one or more sleep qualitymetrics; and apply a respective weighting for each of the one or moresleep quality metrics.
 4. The system of claim 1, wherein the computingdevice is further configured to determine at least one wakeful restingheart rate before detecting the lack of movement.
 5. The system of claim1, wherein the physiological data includes one or more sets of valuesassociated with at least one of: movement, total sleep duration, totaldeep sleep duration, duration of wake time after sleep onset, totalrapid-eye-movement sleep duration, total light sleep duration, breathingpatterns, breathing disturbances, and temperature.
 6. The system ofclaim 1, wherein the computing device is further configured to: collectsleep quality feedback; and determine the score for sleep quality basedat least in part on the collected sleep quality feedback.
 7. The systemof claim 1, wherein the computing device being configured to determinerespective values for the one or more sleep quality metrics includesbeing configured to compare at least one sleeping heart rate and atleast one wakeful resting heart rate.
 8. The system of claim 1, whereinthe computing device is further configured to: detect an onset of sleep;and determine a duration of time from the lack of movement to the onsetof sleep.
 9. The system of claim 8, wherein the computing device isfurther configured to: detect an awake status after the onset of sleep;and determine the respective values for one or more sleep qualitymetrics, in response to detecting the awake status.
 10. Acomputer-implemented method, comprising: detecting a lack of movementfor a threshold period of time; obtaining physiological data includingat least one sleeping heart rate, the physiological data generated by atleast one physiological sensor; determining respective values for one ormore sleep quality metrics based at least in part on the physiologicaldata and at least one wakeful resting heart rate; and determining ascore for sleep quality, based at least in part on the respective valuesof the one or more sleep quality metrics.
 11. The computer-implementedmethod of claim 10, wherein the at least one wakeful resting heart rateincludes a representative value of one or more of heart rate valuesduring a period of relative inactivity.
 12. The computer-implementedmethod of claim 10, wherein the one or more sleep quality metricsincludes a first set of sleep quality metrics associated with sleepquality of a plurality of users, and a second set of sleep qualitymetrics associated with historical sleep quality of one user.
 13. Thecomputer-implemented method of claim 10, wherein determining the scorefor sleep quality includes: determining a respective metric-score foreach of the one or more sleep quality metrics; and applying a respectiveweighting for each of the one or more sleep quality metrics.
 14. Thecomputer-implemented method of claim 10, wherein the at least onewakeful resting heart rate is determined before receiving one or moresignals indicating the lack of movement.
 15. The computer-implementedmethod of claim 10, wherein the physiological data includes one or moresets of values associated with at least one of: movement, total sleepduration, total deep sleep duration, duration of wake time after sleeponset, total rapid-eye-movement sleep duration, total light sleepduration, breathing patterns, breathing disturbances, and temperature.16. The computer-implemented method of claim 10, further comprising:receiving sleep quality feedback; and determining the score for sleepquality based at least in part on the sleep quality feedback.
 17. Thecomputer-implemented method of claim 10, wherein determining therespective values for the one or more sleep quality metrics includescomparing the at least one sleeping heart rate and the at least onewakeful resting heart rate.
 18. The computer-implemented method of claim10, further comprising: receiving at least one signal indicating anonset of sleep; and determining a duration of time from the lack ofmovement to the onset of sleep.
 19. The computer-implemented method ofclaim 18, further comprising: receiving at least one signal indicatingan awake status after the onset of sleep; and determining the respectivevalues for one or more sleep quality metrics, in response to receivingthe at least one signal indicating an awake status.
 20. A computingdevice, comprising: one or more processors; and memory includinginstructions that, when executed by the at least one processor, causethe computing device to: receive movement data from at least one sleeptracking sensor; collect physiological data including at least onesleeping heart rate; determine respective values for one or more sleepquality metrics, based at least in part on the collected physiologicaldata and at least one wakeful resting heart rate; and determine a scorefor sleep quality, based at least in part on the respective values ofthe one or more sleep quality metrics.